---
title: "Moral Values Across Countries"
output:
flexdashboard::flex_dashboard:
orientation: columns
vertical_layout: fill
source_code: embed
---
```{r setup, include=FALSE}
# global options
knitr::opts_chunk$set(echo = FALSE,
tidy = TRUE,
cache = FALSE,
message = FALSE,
error = FALSE,
warning = FALSE)
# packages
library(flexdashboard)
library(here)
library(rio)
library(tidyverse)
library(magrittr)
library(lme4)
library(lmerTest)
library(colorBlindness)
library(dotwhisker)
library(tidytext)
theme_set(theme_minimal()) # set theme
options(scipen=999) # remove scientific notation
```
```{r wrangling, include = FALSE}
# import data
df <- import(here("data", "ALL_MFQ30.csv"), # moral values, countries, & sex
setclass = "tbl_df") %>%
janitor::clean_names()
df_c <- import(here("data", "Data_S1_sec.csv"), # country-level variables
setclass = "tbl_df") %>%
janitor::clean_names()
# data wrangling
df %<>%
drop_na() %>%
mutate(
across(where(is.double), as.numeric),
across(where(is.character), as.factor),
sex = recode(sex,
`1` = "Male",
`0` = "Female",
.default = NA_character_),
indiv = rowMeans(
select(., harm_avg, fairness_avg) # individualizing moral foundations
),
bind = rowMeans(
select(., ingroup_avg:purity_avg) # binding moral foundations
)
)
# check data structure and variables
str(df)
# descriptive statistics by country
c_desc <-
df %>%
pivot_longer(cols = c(indiv, bind),
names_to = "vars",
values_to = "val"
) %>%
select(country, vars, val) %>%
group_by(country, vars) %>%
summarise(mean = mean(val, na.rm = TRUE),
sd = sd(val, na.rm = TRUE),
min = min(val, na.rm = TRUE),
max = max(val, na.rm = TRUE),
.groups = "drop"
) %>%
mutate(vars = fct_recode(vars,
Individualizing = "indiv",
Binding = "bind"
)
)
# descriptive statistics by country and sex
c_s_desc <-
df %>%
filter(country != "Poland") %>% # Poland has missing data in sex.
pivot_longer(cols = c(indiv, bind),
names_to = "vars",
values_to = "val"
) %>%
group_by(country, sex, vars) %>%
summarise(mean = mean(val, na.rm = TRUE),
sd = sd(val, na.rm = TRUE),
min = min(val, na.rm = TRUE),
max = max(val, na.rm = TRUE),
.groups = "drop"
) %>%
mutate(vars = fct_recode(vars,
Individualizing = "indiv",
Binding = "bind"
)
)
```
# Values X Country
Sidebar {.sidebar}
-----------------------------------------------------------------------
**Data**
Data used in this dashboard come from the second study of XX. It's a publicly available dataset, which can be downloaded from XX.
19 countries.
**Summary of the Visualizations**
The first set of plots (on this page) represents the average scores for individualizing and binding foundations across countries. Building on this plot, I next add the sex variable into the mix.
The second set of plots visualize the effect of sex and several country-level variables on the aforementioned foundations.
*Country-level variables*
a
a
a
a
a
a
Column {data-width=600}
-----------------------------------------------------------------------
### Final Version
```{r}
c_desc
```
Column {data-width=400}
-----------------------------------------------------------------------
### Initial version
```{r}
c_desc %>%
ggplot() +
geom_col(
aes(mean, country, fill = vars),
position = "dodge"
) +
theme(
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank()
) +
scale_x_continuous(expand = c(0, 0))
```
> This is the first version of the plot. There is a lot to work on here. Both axes and legend labels seem confusing. X-axis scale is also not complete. Also, it is hard to see any patterns without using facet_wrap and sorting the values.
### Revised version
```{r}
c_desc %>%
ggplot() +
geom_col(
aes(mean, reorder_within(country, mean, vars),
fill = country
)
) +
scale_y_reordered() +
scale_x_continuous(expand = c(0, 0)
) +
facet_wrap(~vars,
scales = "free_y",
ncol = 2) +
theme(
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
legend.position = "none",
axis.text.y = element_text(color = "black",
size = 11),
axis.text.x = element_text(color = "black",
size = 9),
axis.title = element_blank()
) +
labs(
title = "Endorsement of Individualizing and Binding Moral Values Across Countries"
)
```
> The revised version looks much better. The colors are not that nice, though. Also, it'd be nice to see the grand mean to have a general reference category.
# Values X Country X Sex
Column {data-width=600}
-----------------------------------------------------------------------
### Final Version
```{r}
```
Column {data-width=400}
-----------------------------------------------------------------------
### Initial version
```{r}
```
### Revised version
```{r}
```
# Predictors of values
Sidebar {.sidebar}
-----------------------------------------------------------------------
Column {data-width=600}
-----------------------------------------------------------------------
### Final Version
```{r}
```
Column {data-width=400}
-----------------------------------------------------------------------
### Initial version
```{r}
```
### Revised version
```{r}
```
# Predictors of values for each country
Column {data-width=600}
-----------------------------------------------------------------------
### Final Version
```{r}
```
Column {data-width=400}
-----------------------------------------------------------------------
### Initial version
```{r}
```
### Revised version
```{r}
```
# Bonus plots
Sidebar {.sidebar}
-----------------------------------------------------------------------
Column {data-width=600}
-----------------------------------------------------------------------
### Final Version
```{r}
```
Column {data-width=400}
-----------------------------------------------------------------------
### Initial version
```{r}
```
### Revised version
```{r}
```
